------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\rpb0053\Dropbox\Ayal\Trump\data\PS_TrumpRallies.log
  log type:  text
 opened on:  22 Sep 2021, 10:59:06

. 
. /* Full Sample */
. nbreg n_hatecrim rallyL  percent_urban bermanest_percap number_hate violent_crime_percap10k pr
> operty_crime_percap10k percent_rep_pres12 college south northeast midwest jan feb mar apr may 
> jun jul aug sep oc dec, cluster(stcoufips)

Fitting Poisson model:

Iteration 0:   log pseudolikelihood = -103588.51  (not concave)
Iteration 1:   log pseudolikelihood = -95301.454  (not concave)
Iteration 2:   log pseudolikelihood = -87677.337  (not concave)
Iteration 3:   log pseudolikelihood = -84871.856  (not concave)
Iteration 4:   log pseudolikelihood = -80711.228  (not concave)
Iteration 5:   log pseudolikelihood = -79244.108  (not concave)
Iteration 6:   log pseudolikelihood = -78409.249  
Iteration 7:   log pseudolikelihood = -74758.478  (backed up)
Iteration 8:   log pseudolikelihood = -39226.443  (backed up)
Iteration 9:   log pseudolikelihood = -18319.197  (backed up)
Iteration 10:  log pseudolikelihood = -14027.893  
Iteration 11:  log pseudolikelihood =  -9040.673  
Iteration 12:  log pseudolikelihood = -5019.8783  
Iteration 13:  log pseudolikelihood = -3602.5706  
Iteration 14:  log pseudolikelihood = -3530.6266  
Iteration 15:  log pseudolikelihood = -3522.2955  
Iteration 16:  log pseudolikelihood = -3522.2194  
Iteration 17:  log pseudolikelihood = -3522.2193  

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = -5748.2119  (not concave)
Iteration 1:   log pseudolikelihood = -4390.5166  
Iteration 2:   log pseudolikelihood = -4339.7709  
Iteration 3:   log pseudolikelihood = -4339.6961  
Iteration 4:   log pseudolikelihood = -4339.6961  

Fitting full model:

Iteration 0:   log pseudolikelihood = -3986.1881  (not concave)
Iteration 1:   log pseudolikelihood = -3483.2495  
Iteration 2:   log pseudolikelihood = -2900.4361  
Iteration 3:   log pseudolikelihood = -2876.2849  
Iteration 4:   log pseudolikelihood = -2854.6969  
Iteration 5:   log pseudolikelihood = -2854.2939  
Iteration 6:   log pseudolikelihood = -2854.2938  

Negative binomial regression                    Number of obs     =     37,631
                                                Wald chi2(22)     =     920.38
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -2854.2938               Pseudo R2         =     0.3423

                                      (Std. Err. adjusted for 3,137 clusters in stcoufips)
------------------------------------------------------------------------------------------
                         |               Robust
              n_hatecrim |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
                  rallyL |   .8501936     .15916     5.34   0.000     .5382458    1.162142
           percent_urban |   .0700234   .0089156     7.85   0.000      .052549    .0874977
        bermanest_percap |   .0002412   .0000492     4.90   0.000     .0001447    .0003376
             number_hate |   .0187142   .0052325     3.58   0.000     .0084586    .0289697
 violent_crime_percap10k |   .0093286   .0053279     1.75   0.080    -.0011139    .0197711
property_crime_percap10k |  -.0009285   .0021271    -0.44   0.662    -.0050975    .0032406
      percent_rep_pres12 |  -.0364843   .0063997    -5.70   0.000    -.0490274   -.0239412
                 college |   .0346384   .0083405     4.15   0.000     .0182914    .0509855
                   south |  -.9044842   .3227721    -2.80   0.005    -1.537106   -.2718626
               northeast |   .5487281   .2954795     1.86   0.063    -.0304011    1.127857
                 midwest |  -.5134609   .2801626    -1.83   0.067    -1.062569    .0356477
                     jan |  -1.176754   .1712957    -6.87   0.000    -1.512488   -.8410208
                     feb |  -1.224783    .164195    -7.46   0.000      -1.5466    -.902967
                     mar |  -.5899572   .1407012    -4.19   0.000    -.8657264    -.314188
                     apr |  -.9026434   .1575768    -5.73   0.000    -1.211488   -.5937985
                     may |  -.9242171   .1567247    -5.90   0.000    -1.231392   -.6170424
                     jun |  -.8464949   .1791512    -4.73   0.000    -1.197625   -.4953649
                     jul |  -1.302725   .1913331    -6.81   0.000    -1.677731   -.9277188
                     aug |  -1.044355   .1574427    -6.63   0.000    -1.352937   -.7357733
                     sep |  -.9908259   .1586461    -6.25   0.000    -1.301767   -.6798852
                      oc |  -.6555544   .1395287    -4.70   0.000    -.9290255   -.3820832
                     dec |  -.4002304   .1636828    -2.45   0.014    -.7210428   -.0794181
                   _cons |  -4.834941   .5534644    -8.74   0.000    -5.919712   -3.750171
-------------------------+----------------------------------------------------------------
                /lnalpha |   1.300939   .1657122                      .9761485    1.625729
-------------------------+----------------------------------------------------------------
                   alpha |   3.672742   .6086183                      2.654214     5.08212
------------------------------------------------------------------------------------------

. prvalue, x(rallyL=0) rest(mean) save

nbreg: Predictions for n_hatecrim

Confidence intervals by delta method

                                95% Conf. Interval
  Rate:               .00298   [ .00213,    .00382]
  Pr(y=0|x):          0.9970   [ 0.9962,    0.9979]
  Pr(y=1|x):          0.0029   [ 0.0021,    0.0038]
  Pr(y=2|x):          0.0000   [ 0.0000,    0.0000]
  Pr(y=3|x):          0.0000   [ 0.0000,    0.0000]
  Pr(y=4|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=5|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=6|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=7|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=8|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=9|x):          0.0000   [-0.0000,    0.0000]

          rallyL  percent_ur~n  bermanest_~p   number_hate  violent_~10k  property~10k
x=             0     18.503748      314.5418     24.595759     8.6605995     58.120767

    percent_r~12       college         south     northeast       midwest           jan
x=     59.690604     20.783447     .45342935     .06919827     .33610587     .08333555

             feb           mar           apr           may           jun           jul
x=     .08333555     .08333555     .08333555     .08333555     .08333555     .08333555

             aug           sep            oc           dec
x=     .08333555     .08333555     .08333555     .08330897

. prvalue, x(rallyL=1) rest(mean) diff

nbreg: Change in Predictions for n_hatecrim

Confidence intervals by delta method

                     Current     Saved    Change   95% CI for Change
  Rate:               .00696    .00298    .00399  [ 0.0014,   0.0065]
  Pr(y=0|x):          0.9931    0.9970   -0.0039  [-0.0064,  -0.0014]
  Pr(y=1|x):          0.0067    0.0029    0.0038  [ 0.0014,   0.0062]
  Pr(y=2|x):          0.0001    0.0000    0.0001  [ 0.0000,   0.0002]
  Pr(y=3|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=4|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=5|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=6|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=7|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=8|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=9|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]

                rallyL  percent_ur~n  bermanest_~p   number_hate  violent_~10k  property~10k
Current=             1     18.503748      314.5418     24.595759     8.6605995     58.120767
  Saved=             0     18.503748      314.5418     24.595759     8.6605995     58.120767
   Diff=             1             0             0             0             0             0

          percent_r~12       college         south     northeast       midwest           jan
Current=     59.690604     20.783447     .45342935     .06919827     .33610587     .08333555
  Saved=     59.690604     20.783447     .45342935     .06919827     .33610587     .08333555
   Diff=             0             0             0             0             0             0

                   feb           mar           apr           may           jun           jul
Current=     .08333555     .08333555     .08333555     .08333555     .08333555     .08333555
  Saved=     .08333555     .08333555     .08333555     .08333555     .08333555     .08333555
   Diff=             0             0             0             0             0             0

                   aug           sep            oc           dec
Current=     .08333555     .08333555     .08333555     .08330897
  Saved=     .08333555     .08333555     .08333555     .08330897
   Diff=             0             0             0             0

. 
. nbreg n_hatecrim rallyL      percent_urban bermanest_percap number_hate violent_crime_percap10
> k property_crime_percap10k percent_rep_pres12 college south northeast midwest jan feb mar apr 
> may jun jul aug sep oc dec, cluster(stcoufips) irr

Fitting Poisson model:

Iteration 0:   log pseudolikelihood = -103588.51  (not concave)
Iteration 1:   log pseudolikelihood = -95301.454  (not concave)
Iteration 2:   log pseudolikelihood = -87677.337  (not concave)
Iteration 3:   log pseudolikelihood = -84871.856  (not concave)
Iteration 4:   log pseudolikelihood = -80711.228  (not concave)
Iteration 5:   log pseudolikelihood = -79244.108  (not concave)
Iteration 6:   log pseudolikelihood = -78409.249  
Iteration 7:   log pseudolikelihood = -74758.478  (backed up)
Iteration 8:   log pseudolikelihood = -39226.443  (backed up)
Iteration 9:   log pseudolikelihood = -18319.197  (backed up)
Iteration 10:  log pseudolikelihood = -14027.893  
Iteration 11:  log pseudolikelihood =  -9040.673  
Iteration 12:  log pseudolikelihood = -5019.8783  
Iteration 13:  log pseudolikelihood = -3602.5706  
Iteration 14:  log pseudolikelihood = -3530.6266  
Iteration 15:  log pseudolikelihood = -3522.2955  
Iteration 16:  log pseudolikelihood = -3522.2194  
Iteration 17:  log pseudolikelihood = -3522.2193  

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = -5748.2119  (not concave)
Iteration 1:   log pseudolikelihood = -4390.5166  
Iteration 2:   log pseudolikelihood = -4339.7709  
Iteration 3:   log pseudolikelihood = -4339.6961  
Iteration 4:   log pseudolikelihood = -4339.6961  

Fitting full model:

Iteration 0:   log pseudolikelihood = -3986.1881  (not concave)
Iteration 1:   log pseudolikelihood = -3483.2495  
Iteration 2:   log pseudolikelihood = -2900.4361  
Iteration 3:   log pseudolikelihood = -2876.2849  
Iteration 4:   log pseudolikelihood = -2854.6969  
Iteration 5:   log pseudolikelihood = -2854.2939  
Iteration 6:   log pseudolikelihood = -2854.2938  

Negative binomial regression                    Number of obs     =     37,631
                                                Wald chi2(22)     =     920.38
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -2854.2938               Pseudo R2         =     0.3423

                                      (Std. Err. adjusted for 3,137 clusters in stcoufips)
------------------------------------------------------------------------------------------
                         |               Robust
              n_hatecrim |        IRR   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
                  rallyL |     2.3401   .3724503     5.34   0.000     1.712999    3.196772
           percent_urban |   1.072533   .0095623     7.85   0.000     1.053954     1.09144
        bermanest_percap |   1.000241   .0000492     4.90   0.000     1.000145    1.000338
             number_hate |    1.01889   .0053314     3.58   0.000     1.008494    1.029393
 violent_crime_percap10k |   1.009372   .0053778     1.75   0.080     .9988867    1.019968
property_crime_percap10k |    .999072   .0021252    -0.44   0.662     .9949154    1.003246
      percent_rep_pres12 |   .9641732   .0061704    -5.70   0.000      .952155    .9763431
                 college |   1.035245   .0086344     4.15   0.000      1.01846    1.052308
                   south |   .4047506   .1306422    -2.80   0.005     .2150025    .7619589
               northeast |    1.73105   .5114898     1.86   0.063     .9700564    3.089031
                 midwest |   .5984209   .1676551    -1.83   0.067     .3455668    1.036291
                     jan |   .3082777   .0528066    -6.87   0.000     .2203611    .4312701
                     feb |   .2938214    .048244    -7.46   0.000     .2129709    .4053652
                     mar |    .554351   .0779978    -4.19   0.000     .4207458    .7303817
                     apr |   .4054963   .0638968    -5.73   0.000     .2977538    .5522257
                     may |    .396842   .0621949    -5.90   0.000      .291886    .5395378
                     jun |   .4289157   .0768408    -4.73   0.000     .3019104    .6093485
                     jul |   .2717902   .0520025    -6.81   0.000     .1867974    .3954548
                     aug |   .3519186    .055407    -6.63   0.000     .2584799    .4791348
                     sep |   .3712699   .0589005    -6.25   0.000     .2720508    .5066752
                      oc |   .5191542   .0724369    -4.70   0.000     .3949384    .6824383
                     dec |   .6701656   .1096946    -2.45   0.014     .4862449    .9236537
                   _cons |   .0079472   .0043985    -8.74   0.000      .002686    .0235137
-------------------------+----------------------------------------------------------------
                /lnalpha |   1.300939   .1657122                      .9761485    1.625729
-------------------------+----------------------------------------------------------------
                   alpha |   3.672742   .6086183                      2.654214     5.08212
------------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Note: _cons estimates baseline incidence rate.

. gen sample=1 if e(sample)
(134 missing values generated)

. eststo lagged

. prvalue, x(rallyL=0) rest(mean) save

nbreg: Predictions for n_hatecrim

Confidence intervals by delta method

                                95% Conf. Interval
  Rate:               .00298   [ .00213,    .00382]
  Pr(y=0|x):          0.9970   [ 0.9962,    0.9979]
  Pr(y=1|x):          0.0029   [ 0.0021,    0.0038]
  Pr(y=2|x):          0.0000   [ 0.0000,    0.0000]
  Pr(y=3|x):          0.0000   [ 0.0000,    0.0000]
  Pr(y=4|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=5|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=6|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=7|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=8|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=9|x):          0.0000   [-0.0000,    0.0000]

          rallyL  percent_ur~n  bermanest_~p   number_hate  violent_~10k  property~10k
x=             0     18.503748      314.5418     24.595759     8.6605995     58.120767

    percent_r~12       college         south     northeast       midwest           jan
x=     59.690604     20.783447     .45342935     .06919827     .33610587     .08333555

             feb           mar           apr           may           jun           jul
x=     .08333555     .08333555     .08333555     .08333555     .08333555     .08333555

             aug           sep            oc           dec
x=     .08333555     .08333555     .08333555     .08330897

. prvalue, x(rallyL=1) rest(mean) diff

nbreg: Change in Predictions for n_hatecrim

Confidence intervals by delta method

                     Current     Saved    Change   95% CI for Change
  Rate:               .00696    .00298    .00399  [ 0.0014,   0.0065]
  Pr(y=0|x):          0.9931    0.9970   -0.0039  [-0.0064,  -0.0014]
  Pr(y=1|x):          0.0067    0.0029    0.0038  [ 0.0014,   0.0062]
  Pr(y=2|x):          0.0001    0.0000    0.0001  [ 0.0000,   0.0002]
  Pr(y=3|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=4|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=5|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=6|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=7|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=8|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=9|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]

                rallyL  percent_ur~n  bermanest_~p   number_hate  violent_~10k  property~10k
Current=             1     18.503748      314.5418     24.595759     8.6605995     58.120767
  Saved=             0     18.503748      314.5418     24.595759     8.6605995     58.120767
   Diff=             1             0             0             0             0             0

          percent_r~12       college         south     northeast       midwest           jan
Current=     59.690604     20.783447     .45342935     .06919827     .33610587     .08333555
  Saved=     59.690604     20.783447     .45342935     .06919827     .33610587     .08333555
   Diff=             0             0             0             0             0             0

                   feb           mar           apr           may           jun           jul
Current=     .08333555     .08333555     .08333555     .08333555     .08333555     .08333555
  Saved=     .08333555     .08333555     .08333555     .08333555     .08333555     .08333555
   Diff=             0             0             0             0             0             0

                   aug           sep            oc           dec
Current=     .08333555     .08333555     .08333555     .08330897
  Saved=     .08333555     .08333555     .08333555     .08330897
   Diff=             0             0             0             0

. gen pipe = "|"

. 
. 
. /* Matched Models */
. nbreg n_hatecrim rallyL percent_urban bermanest_percap number_hate violent_crime_percap10k pro
> perty_crime_percap10k percent_rep_pres12 college south northeast midwest jan feb mar apr may j
> un jul aug sep oc dec [iweight=cem_weights], cluster(stcoufips) 

Fitting Poisson model:

Iteration 0:   log pseudolikelihood = -50343.898  (not concave)
Iteration 1:   log pseudolikelihood = -46510.323  
Iteration 2:   log pseudolikelihood = -44669.539  (backed up)
Iteration 3:   log pseudolikelihood = -35801.954  (backed up)
Iteration 4:   log pseudolikelihood = -21691.391  (backed up)
Iteration 5:   log pseudolikelihood = -14191.234  
Iteration 6:   log pseudolikelihood = -12081.445  
Iteration 7:   log pseudolikelihood =  -5267.575  
Iteration 8:   log pseudolikelihood = -5076.3833  
Iteration 9:   log pseudolikelihood = -5071.0963  
Iteration 10:  log pseudolikelihood = -5071.0849  
Iteration 11:  log pseudolikelihood = -5071.0849  

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = -7445.7744  (not concave)
Iteration 1:   log pseudolikelihood = -6354.1109  
Iteration 2:   log pseudolikelihood =  -6207.116  
Iteration 3:   log pseudolikelihood = -6203.0537  
Iteration 4:   log pseudolikelihood = -6203.0537  

Fitting full model:

Iteration 0:   log pseudolikelihood = -5705.2263  (not concave)
Iteration 1:   log pseudolikelihood = -4933.0335  
Iteration 2:   log pseudolikelihood = -4783.1368  (not concave)
Iteration 3:   log pseudolikelihood =  -4582.079  
Iteration 4:   log pseudolikelihood =  -4540.012  
Iteration 5:   log pseudolikelihood = -4525.0306  
Iteration 6:   log pseudolikelihood =   -4524.88  
Iteration 7:   log pseudolikelihood = -4524.8799  

Negative binomial regression                    Number of obs     =     16,368
                                                Wald chi2(22)     =     970.97
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -4524.8799               Pseudo R2         =     0.2705

                                      (Std. Err. adjusted for 1,364 clusters in stcoufips)
------------------------------------------------------------------------------------------
                         |               Robust
              n_hatecrim |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
                  rallyL |   .5309852   .1576051     3.37   0.001     .2220849    .8398855
           percent_urban |   .0516185   .0125554     4.11   0.000     .0270103    .0762267
        bermanest_percap |   .0002779   .0000674     4.12   0.000     .0001458      .00041
             number_hate |   .0054488   .0069693     0.78   0.434    -.0082107    .0191083
 violent_crime_percap10k |  -.0063492   .0043366    -1.46   0.143    -.0148487    .0021503
property_crime_percap10k |   .0049685    .001968     2.52   0.012     .0011114    .0088257
      percent_rep_pres12 |  -.0492975   .0139329    -3.54   0.000    -.0766055   -.0219895
                 college |   .0497416   .0140974     3.53   0.000     .0221112    .0773721
                   south |  -.3773834   .4234615    -0.89   0.373    -1.207353     .452586
               northeast |   .3016816   .4061528     0.74   0.458    -.4943633    1.097726
                 midwest |  -.6157699   .4565185    -1.35   0.177     -1.51053      .27899
                     jan |  -.8461974   .2427314    -3.49   0.000    -1.321942   -.3704525
                     feb |  -1.468573   .2730337    -5.38   0.000    -2.003709   -.9334371
                     mar |  -.8286195   .2169986    -3.82   0.000    -1.253929   -.4033099
                     apr |  -.9339761   .2593555    -3.60   0.000    -1.442304   -.4256487
                     may |  -.9831392   .2598824    -3.78   0.000    -1.492499    -.473779
                     jun |  -1.143914   .3202143    -3.57   0.000    -1.771523   -.5163057
                     jul |  -1.351696   .2573053    -5.25   0.000    -1.856005   -.8473868
                     aug |   -1.31445   .2042565    -6.44   0.000    -1.714785   -.9141142
                     sep |  -1.392621   .2612905    -5.33   0.000    -1.904741   -.8805014
                      oc |  -.9294058   .2085238    -4.46   0.000    -1.338105   -.5207067
                     dec |  -.5825636    .250375    -2.33   0.020    -1.073289   -.0918377
                   _cons |   -3.55406   .9550902    -3.72   0.000    -5.426002   -1.682118
-------------------------+----------------------------------------------------------------
                /lnalpha |       .603   .3269798                     -.0378686    1.243869
-------------------------+----------------------------------------------------------------
                   alpha |   1.827593    .597586                      .9628395    3.469007
------------------------------------------------------------------------------------------

. prvalue, x(rallyL=0) rest(mean) save

nbreg: Predictions for n_hatecrim

Confidence intervals by delta method

                                95% Conf. Interval
  Rate:               .00681   [ .00295,    .01067]
  Pr(y=0|x):          0.9933   [ 0.9895,    0.9970]
  Pr(y=1|x):          0.0067   [ 0.0030,    0.0104]
  Pr(y=2|x):          0.0001   [-0.0000,    0.0001]
  Pr(y=3|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=4|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=5|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=6|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=7|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=8|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=9|x):          0.0000   [-0.0000,    0.0000]

          rallyL  percent_ur~n  bermanest_~p   number_hate  violent_~10k  property~10k
x=             0     21.630606     370.25021     25.559384       7.96612     55.403697

    percent_r~12       college         south     northeast       midwest           jan
x=     56.332322     22.346481     .45601173     .09090909     .36876833     .08333333

             feb           mar           apr           may           jun           jul
x=     .08333333     .08333333     .08333333     .08333333     .08333333     .08333333

             aug           sep            oc           dec
x=     .08333333     .08333333     .08333333     .08333333

. prvalue, x(rallyL=1) rest(mean) diff

nbreg: Change in Predictions for n_hatecrim

Confidence intervals by delta method

                     Current     Saved    Change   95% CI for Change
  Rate:               .01158    .00681    .00477  [ 0.0005,   0.0090]
  Pr(y=0|x):          0.9886    0.9933   -0.0047  [-0.0087,  -0.0006]
  Pr(y=1|x):          0.0112    0.0067    0.0045  [ 0.0006,   0.0085]
  Pr(y=2|x):          0.0002    0.0001    0.0001  [-0.0000,   0.0003]
  Pr(y=3|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=4|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=5|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=6|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=7|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=8|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=9|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]

                rallyL  percent_ur~n  bermanest_~p   number_hate  violent_~10k  property~10k
Current=             1     21.630606     370.25021     25.559384       7.96612     55.403697
  Saved=             0     21.630606     370.25021     25.559384       7.96612     55.403697
   Diff=             1             0             0             0             0             0

          percent_r~12       college         south     northeast       midwest           jan
Current=     56.332322     22.346481     .45601173     .09090909     .36876833     .08333333
  Saved=     56.332322     22.346481     .45601173     .09090909     .36876833     .08333333
   Diff=             0             0             0             0             0             0

                   feb           mar           apr           may           jun           jul
Current=     .08333333     .08333333     .08333333     .08333333     .08333333     .08333333
  Saved=     .08333333     .08333333     .08333333     .08333333     .08333333     .08333333
   Diff=             0             0             0             0             0             0

                   aug           sep            oc           dec
Current=     .08333333     .08333333     .08333333     .08333333
  Saved=     .08333333     .08333333     .08333333     .08333333
   Diff=             0             0             0             0

. 
. nbreg n_hatecrim rallyL percent_urban bermanest_percap number_hate violent_crime_percap10k pro
> perty_crime_percap10k percent_rep_pres12 college south northeast midwest jan feb mar apr may j
> un jul aug sep oc dec [iweight=cem_weights], cluster(stcoufips) irr

Fitting Poisson model:

Iteration 0:   log pseudolikelihood = -50343.898  (not concave)
Iteration 1:   log pseudolikelihood = -46510.323  
Iteration 2:   log pseudolikelihood = -44669.539  (backed up)
Iteration 3:   log pseudolikelihood = -35801.954  (backed up)
Iteration 4:   log pseudolikelihood = -21691.391  (backed up)
Iteration 5:   log pseudolikelihood = -14191.234  
Iteration 6:   log pseudolikelihood = -12081.445  
Iteration 7:   log pseudolikelihood =  -5267.575  
Iteration 8:   log pseudolikelihood = -5076.3833  
Iteration 9:   log pseudolikelihood = -5071.0963  
Iteration 10:  log pseudolikelihood = -5071.0849  
Iteration 11:  log pseudolikelihood = -5071.0849  

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = -7445.7744  (not concave)
Iteration 1:   log pseudolikelihood = -6354.1109  
Iteration 2:   log pseudolikelihood =  -6207.116  
Iteration 3:   log pseudolikelihood = -6203.0537  
Iteration 4:   log pseudolikelihood = -6203.0537  

Fitting full model:

Iteration 0:   log pseudolikelihood = -5705.2263  (not concave)
Iteration 1:   log pseudolikelihood = -4933.0335  
Iteration 2:   log pseudolikelihood = -4783.1368  (not concave)
Iteration 3:   log pseudolikelihood =  -4582.079  
Iteration 4:   log pseudolikelihood =  -4540.012  
Iteration 5:   log pseudolikelihood = -4525.0306  
Iteration 6:   log pseudolikelihood =   -4524.88  
Iteration 7:   log pseudolikelihood = -4524.8799  

Negative binomial regression                    Number of obs     =     16,368
                                                Wald chi2(22)     =     970.97
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -4524.8799               Pseudo R2         =     0.2705

                                      (Std. Err. adjusted for 1,364 clusters in stcoufips)
------------------------------------------------------------------------------------------
                         |               Robust
              n_hatecrim |        IRR   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
                  rallyL |   1.700607   .2680243     3.37   0.001     1.248677    2.316102
           percent_urban |   1.052974   .0132205     4.11   0.000     1.027378    1.079207
        bermanest_percap |   1.000278   .0000674     4.12   0.000     1.000146     1.00041
             number_hate |   1.005464   .0070073     0.78   0.434     .9918229    1.019292
 violent_crime_percap10k |   .9936709   .0043091    -1.46   0.143      .985261    1.002153
property_crime_percap10k |   1.004981   .0019778     2.52   0.012     1.001112    1.008865
      percent_rep_pres12 |   .9518979   .0132627    -3.54   0.000     .9262552    .9782505
                 college |      1.051   .0148164     3.53   0.000     1.022357    1.080444
                   south |   .6856532   .2903477    -0.89   0.373     .2989877    1.572373
               northeast |   1.352131   .5491716     0.74   0.458     .6099592    2.997344
                 midwest |   .5402248   .2466227    -1.35   0.177      .220793    1.321794
                     jan |   .4290433   .1041423    -3.49   0.000      .266617    .6904219
                     feb |   .2302538    .062867    -5.38   0.000     .1348342    .3931999
                     mar |   .4366517   .0947528    -3.82   0.000     .2853813     .668105
                     apr |    .392988   .1019236    -3.60   0.000     .2363826    .6533458
                     may |   .3741348    .097231    -3.78   0.000     .2248101    .6226448
                     jun |   .3185696   .1020106    -3.57   0.000     .1700738    .5967209
                     jul |    .258801   .0665909    -5.25   0.000     .1562958    .4285333
                     aug |   .2686222   .0548678    -6.44   0.000     .1800024    .4008716
                     sep |   .2484232   .0649106    -5.33   0.000     .1488611     .414575
                      oc |   .3947882   .0823227    -4.46   0.000     .2623424    .5941006
                     dec |   .5584649   .1398256    -2.33   0.020     .3418821    .9122532
                   _cons |   .0286083   .0273235    -3.72   0.000     .0044007    .1859797
-------------------------+----------------------------------------------------------------
                /lnalpha |       .603   .3269798                     -.0378686    1.243869
-------------------------+----------------------------------------------------------------
                   alpha |   1.827593    .597586                      .9628395    3.469007
------------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Note: _cons estimates baseline incidence rate.

. prvalue, x(rallyL=0) rest(mean) save

nbreg: Predictions for n_hatecrim

Confidence intervals by delta method

                                95% Conf. Interval
  Rate:               .00681   [ .00295,    .01067]
  Pr(y=0|x):          0.9933   [ 0.9895,    0.9970]
  Pr(y=1|x):          0.0067   [ 0.0030,    0.0104]
  Pr(y=2|x):          0.0001   [-0.0000,    0.0001]
  Pr(y=3|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=4|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=5|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=6|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=7|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=8|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=9|x):          0.0000   [-0.0000,    0.0000]

          rallyL  percent_ur~n  bermanest_~p   number_hate  violent_~10k  property~10k
x=             0     21.630606     370.25021     25.559384       7.96612     55.403697

    percent_r~12       college         south     northeast       midwest           jan
x=     56.332322     22.346481     .45601173     .09090909     .36876833     .08333333

             feb           mar           apr           may           jun           jul
x=     .08333333     .08333333     .08333333     .08333333     .08333333     .08333333

             aug           sep            oc           dec
x=     .08333333     .08333333     .08333333     .08333333

. prvalue, x(rallyL=1) rest(mean) diff

nbreg: Change in Predictions for n_hatecrim

Confidence intervals by delta method

                     Current     Saved    Change   95% CI for Change
  Rate:               .01158    .00681    .00477  [ 0.0005,   0.0090]
  Pr(y=0|x):          0.9886    0.9933   -0.0047  [-0.0087,  -0.0006]
  Pr(y=1|x):          0.0112    0.0067    0.0045  [ 0.0006,   0.0085]
  Pr(y=2|x):          0.0002    0.0001    0.0001  [-0.0000,   0.0003]
  Pr(y=3|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=4|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=5|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=6|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=7|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=8|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=9|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]

                rallyL  percent_ur~n  bermanest_~p   number_hate  violent_~10k  property~10k
Current=             1     21.630606     370.25021     25.559384       7.96612     55.403697
  Saved=             0     21.630606     370.25021     25.559384       7.96612     55.403697
   Diff=             1             0             0             0             0             0

          percent_r~12       college         south     northeast       midwest           jan
Current=     56.332322     22.346481     .45601173     .09090909     .36876833     .08333333
  Saved=     56.332322     22.346481     .45601173     .09090909     .36876833     .08333333
   Diff=             0             0             0             0             0             0

                   feb           mar           apr           may           jun           jul
Current=     .08333333     .08333333     .08333333     .08333333     .08333333     .08333333
  Saved=     .08333333     .08333333     .08333333     .08333333     .08333333     .08333333
   Diff=             0             0             0             0             0             0

                   aug           sep            oc           dec
Current=     .08333333     .08333333     .08333333     .08333333
  Saved=     .08333333     .08333333     .08333333     .08333333
   Diff=             0             0             0             0

. 
. 
. /* Appendix */
. /* Table A */
. nbreg n_hatecrim postrally2 percent_urban bermanest_percap number_hate violent_crime_percap10k
>  property_crime_percap10k percent_rep_pres12 college south northeast midwest jan feb mar apr m
> ay jun jul aug sep oc dec, cluster(stcoufips) 

Fitting Poisson model:

Iteration 0:   log pseudolikelihood = -102539.86  (not concave)
Iteration 1:   log pseudolikelihood = -94336.683  (not concave)
Iteration 2:   log pseudolikelihood = -86789.748  (not concave)
Iteration 3:   log pseudolikelihood = -84012.882  (not concave)
Iteration 4:   log pseudolikelihood =  -81981.81  (not concave)
Iteration 5:   log pseudolikelihood = -79010.235  (not concave)
Iteration 6:   log pseudolikelihood = -78117.558  (not concave)
Iteration 7:   log pseudolikelihood = -71932.176  
Iteration 8:   log pseudolikelihood =  -25673.36  (backed up)
Iteration 9:   log pseudolikelihood = -17907.866  (backed up)
Iteration 10:  log pseudolikelihood = -9538.2779  (backed up)
Iteration 11:  log pseudolikelihood = -4995.9533  
Iteration 12:  log pseudolikelihood = -4044.9284  
Iteration 13:  log pseudolikelihood = -3423.4785  
Iteration 14:  log pseudolikelihood =  -3407.719  
Iteration 15:  log pseudolikelihood = -3407.6208  
Iteration 16:  log pseudolikelihood = -3407.6208  

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = -5748.2119  (not concave)
Iteration 1:   log pseudolikelihood = -4390.5166  
Iteration 2:   log pseudolikelihood = -4339.7709  
Iteration 3:   log pseudolikelihood = -4339.6961  
Iteration 4:   log pseudolikelihood = -4339.6961  

Fitting full model:

Iteration 0:   log pseudolikelihood = -3991.4547  (not concave)
Iteration 1:   log pseudolikelihood = -3493.6266  
Iteration 2:   log pseudolikelihood = -2907.0766  
Iteration 3:   log pseudolikelihood = -2895.8157  
Iteration 4:   log pseudolikelihood = -2838.7985  
Iteration 5:   log pseudolikelihood = -2835.8302  
Iteration 6:   log pseudolikelihood = -2835.8222  
Iteration 7:   log pseudolikelihood = -2835.8222  

Negative binomial regression                    Number of obs     =     37,631
                                                Wald chi2(22)     =     982.12
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -2835.8222               Pseudo R2         =     0.3465

                                      (Std. Err. adjusted for 3,137 clusters in stcoufips)
------------------------------------------------------------------------------------------
                         |               Robust
              n_hatecrim |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
              postrally2 |   .8314845   .1710238     4.86   0.000      .496284    1.166685
           percent_urban |   .0649315   .0085037     7.64   0.000     .0482645    .0815985
        bermanest_percap |   .0002402   .0000519     4.63   0.000     .0001385    .0003418
             number_hate |   .0186707   .0051736     3.61   0.000     .0085306    .0288108
 violent_crime_percap10k |   .0093362    .005178     1.80   0.071    -.0008124    .0194849
property_crime_percap10k |  -.0013075   .0020042    -0.65   0.514    -.0052356    .0026206
      percent_rep_pres12 |  -.0371764   .0064308    -5.78   0.000    -.0497806   -.0245723
                 college |   .0311785   .0079926     3.90   0.000     .0155133    .0468437
                   south |  -.9169738   .3137936    -2.92   0.003    -1.531998   -.3019496
               northeast |   .5092591   .2977125     1.71   0.087    -.0742467    1.092765
                 midwest |  -.5952423   .2693415    -2.21   0.027    -1.123142   -.0673427
                     jan |  -1.001672    .179033    -5.59   0.000     -1.35257   -.6507736
                     feb |  -1.035091   .1578403    -6.56   0.000    -1.344452   -.7257294
                     mar |  -.4432886   .1429866    -3.10   0.002    -.7235372     -.16304
                     apr |  -.7797043   .1632385    -4.78   0.000    -1.099646   -.4597628
                     may |  -.8046777   .1611383    -4.99   0.000    -1.120503   -.4888524
                     jun |   -.767973   .1768875    -4.34   0.000    -1.114666   -.4212798
                     jul |  -1.236189   .1945593    -6.35   0.000    -1.617518   -.8548596
                     aug |  -.9955307    .158344    -6.29   0.000    -1.305879   -.6851823
                     sep |  -.9575729   .1586823    -6.03   0.000    -1.268584   -.6465614
                      oc |  -.6746163   .1406157    -4.80   0.000     -.950218   -.3990146
                     dec |  -.4362044   .1621347    -2.69   0.007    -.7539826   -.1184261
                   _cons |   -4.66395   .5395901    -8.64   0.000    -5.721528   -3.606373
-------------------------+----------------------------------------------------------------
                /lnalpha |   1.234492   .1800064                       .881686    1.587298
-------------------------+----------------------------------------------------------------
                   alpha |   3.436632   .6186159                      2.414968    4.890518
------------------------------------------------------------------------------------------

. prvalue, x(postrally2=0) rest(mean) save

nbreg: Predictions for n_hatecrim

Confidence intervals by delta method

                                95% Conf. Interval
  Rate:               .00292   [  .0021,    .00374]
  Pr(y=0|x):          0.9971   [ 0.9963,    0.9979]
  Pr(y=1|x):          0.0029   [ 0.0021,    0.0037]
  Pr(y=2|x):          0.0000   [ 0.0000,    0.0000]
  Pr(y=3|x):          0.0000   [ 0.0000,    0.0000]
  Pr(y=4|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=5|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=6|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=7|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=8|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=9|x):          0.0000   [-0.0000,    0.0000]

      postrally2  percent_ur~n  bermanest_~p   number_hate  violent_~10k  property~10k
x=             0     18.503748      314.5418     24.595759     8.6605995     58.120767

    percent_r~12       college         south     northeast       midwest           jan
x=     59.690604     20.783447     .45342935     .06919827     .33610587     .08333555

             feb           mar           apr           may           jun           jul
x=     .08333555     .08333555     .08333555     .08333555     .08333555     .08333555

             aug           sep            oc           dec
x=     .08333555     .08333555     .08333555     .08330897

. prvalue, x(postrally2=1) rest(mean) diff

nbreg: Change in Predictions for n_hatecrim

Confidence intervals by delta method

                     Current     Saved    Change   95% CI for Change
  Rate:               .00671    .00292    .00379  [ 0.0012,   0.0064]
  Pr(y=0|x):          0.9934    0.9971   -0.0037  [-0.0062,  -0.0012]
  Pr(y=1|x):          0.0065    0.0029    0.0036  [ 0.0012,   0.0060]
  Pr(y=2|x):          0.0001    0.0000    0.0001  [-0.0000,   0.0002]
  Pr(y=3|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=4|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=5|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=6|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=7|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=8|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=9|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]

            postrally2  percent_ur~n  bermanest_~p   number_hate  violent_~10k  property~10k
Current=             1     18.503748      314.5418     24.595759     8.6605995     58.120767
  Saved=             0     18.503748      314.5418     24.595759     8.6605995     58.120767
   Diff=             1             0             0             0             0             0

          percent_r~12       college         south     northeast       midwest           jan
Current=     59.690604     20.783447     .45342935     .06919827     .33610587     .08333555
  Saved=     59.690604     20.783447     .45342935     .06919827     .33610587     .08333555
   Diff=             0             0             0             0             0             0

                   feb           mar           apr           may           jun           jul
Current=     .08333555     .08333555     .08333555     .08333555     .08333555     .08333555
  Saved=     .08333555     .08333555     .08333555     .08333555     .08333555     .08333555
   Diff=             0             0             0             0             0             0

                   aug           sep            oc           dec
Current=     .08333555     .08333555     .08333555     .08330897
  Saved=     .08333555     .08333555     .08333555     .08330897
   Diff=             0             0             0             0

. 
. nbreg n_hatecrim postrally2 percent_urban bermanest_percap number_hate violent_crime_percap10k
>  property_crime_percap10k percent_rep_pres12 college south northeast midwest jan feb mar apr m
> ay jun jul aug sep oc dec, cluster(stcoufips) irr

Fitting Poisson model:

Iteration 0:   log pseudolikelihood = -102539.86  (not concave)
Iteration 1:   log pseudolikelihood = -94336.683  (not concave)
Iteration 2:   log pseudolikelihood = -86789.748  (not concave)
Iteration 3:   log pseudolikelihood = -84012.882  (not concave)
Iteration 4:   log pseudolikelihood =  -81981.81  (not concave)
Iteration 5:   log pseudolikelihood = -79010.235  (not concave)
Iteration 6:   log pseudolikelihood = -78117.558  (not concave)
Iteration 7:   log pseudolikelihood = -71932.176  
Iteration 8:   log pseudolikelihood =  -25673.36  (backed up)
Iteration 9:   log pseudolikelihood = -17907.866  (backed up)
Iteration 10:  log pseudolikelihood = -9538.2779  (backed up)
Iteration 11:  log pseudolikelihood = -4995.9533  
Iteration 12:  log pseudolikelihood = -4044.9284  
Iteration 13:  log pseudolikelihood = -3423.4785  
Iteration 14:  log pseudolikelihood =  -3407.719  
Iteration 15:  log pseudolikelihood = -3407.6208  
Iteration 16:  log pseudolikelihood = -3407.6208  

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = -5748.2119  (not concave)
Iteration 1:   log pseudolikelihood = -4390.5166  
Iteration 2:   log pseudolikelihood = -4339.7709  
Iteration 3:   log pseudolikelihood = -4339.6961  
Iteration 4:   log pseudolikelihood = -4339.6961  

Fitting full model:

Iteration 0:   log pseudolikelihood = -3991.4547  (not concave)
Iteration 1:   log pseudolikelihood = -3493.6266  
Iteration 2:   log pseudolikelihood = -2907.0766  
Iteration 3:   log pseudolikelihood = -2895.8157  
Iteration 4:   log pseudolikelihood = -2838.7985  
Iteration 5:   log pseudolikelihood = -2835.8302  
Iteration 6:   log pseudolikelihood = -2835.8222  
Iteration 7:   log pseudolikelihood = -2835.8222  

Negative binomial regression                    Number of obs     =     37,631
                                                Wald chi2(22)     =     982.12
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood = -2835.8222               Pseudo R2         =     0.3465

                                      (Std. Err. adjusted for 3,137 clusters in stcoufips)
------------------------------------------------------------------------------------------
                         |               Robust
              n_hatecrim |        IRR   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
              postrally2 |   2.296726   .3927948     4.86   0.000     1.642606    3.211329
           percent_urban |   1.067086   .0090742     7.64   0.000     1.049448     1.08502
        bermanest_percap |    1.00024   .0000519     4.63   0.000     1.000139    1.000342
             number_hate |   1.018846   .0052711     3.61   0.000     1.008567     1.02923
 violent_crime_percap10k |    1.00938   .0052266     1.80   0.071     .9991879    1.019676
property_crime_percap10k |   .9986934   .0020016    -0.65   0.514      .994778    1.002624
      percent_rep_pres12 |   .9635061   .0061961    -5.78   0.000     .9514381    .9757272
                 college |    1.03167   .0082457     3.90   0.000     1.015634    1.047958
                   south |   .3997269   .1254317    -2.92   0.003     .2161035    .7393753
               northeast |   1.664058   .4954108     1.71   0.087     .9284426    2.982509
                 midwest |   .5514289   .1485227    -2.21   0.027     .3252562    .9348748
                     jan |    .367265   .0657525    -5.59   0.000     .2585749    .5216421
                     feb |   .3551941    .056064    -6.56   0.000     .2606825    .4839714
                     mar |   .6419219   .0917862    -3.10   0.002     .4850336    .8495572
                     apr |   .4585416   .0748516    -4.78   0.000      .332989    .6314334
                     may |   .4472321   .0720662    -4.99   0.000     .3261157    .6133298
                     jun |   .4639526   .0820674    -4.34   0.000     .3280248    .6562065
                     jul |   .2904892   .0565174    -6.35   0.000     .1983905    .4253429
                     aug |   .3695273   .0585124    -6.29   0.000     .2709342    .5039984
                     sep |   .3838233    .060906    -6.03   0.000     .2812294     .523844
                      oc |   .5093518   .0716229    -4.80   0.000     .3866567    .6709809
                     dec |   .6464856   .1048178    -2.69   0.007      .470489    .8883174
                   _cons |   .0094291   .0050879    -8.64   0.000     .0032747    .0271501
-------------------------+----------------------------------------------------------------
                /lnalpha |   1.234492   .1800064                       .881686    1.587298
-------------------------+----------------------------------------------------------------
                   alpha |   3.436632   .6186159                      2.414968    4.890518
------------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Note: _cons estimates baseline incidence rate.

. prvalue, x(postrally2=0) rest(mean) save

nbreg: Predictions for n_hatecrim

Confidence intervals by delta method

                                95% Conf. Interval
  Rate:               .00292   [  .0021,    .00374]
  Pr(y=0|x):          0.9971   [ 0.9963,    0.9979]
  Pr(y=1|x):          0.0029   [ 0.0021,    0.0037]
  Pr(y=2|x):          0.0000   [ 0.0000,    0.0000]
  Pr(y=3|x):          0.0000   [ 0.0000,    0.0000]
  Pr(y=4|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=5|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=6|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=7|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=8|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=9|x):          0.0000   [-0.0000,    0.0000]

      postrally2  percent_ur~n  bermanest_~p   number_hate  violent_~10k  property~10k
x=             0     18.503748      314.5418     24.595759     8.6605995     58.120767

    percent_r~12       college         south     northeast       midwest           jan
x=     59.690604     20.783447     .45342935     .06919827     .33610587     .08333555

             feb           mar           apr           may           jun           jul
x=     .08333555     .08333555     .08333555     .08333555     .08333555     .08333555

             aug           sep            oc           dec
x=     .08333555     .08333555     .08333555     .08330897

. prvalue, x(postrally2=1) rest(mean) diff

nbreg: Change in Predictions for n_hatecrim

Confidence intervals by delta method

                     Current     Saved    Change   95% CI for Change
  Rate:               .00671    .00292    .00379  [ 0.0012,   0.0064]
  Pr(y=0|x):          0.9934    0.9971   -0.0037  [-0.0062,  -0.0012]
  Pr(y=1|x):          0.0065    0.0029    0.0036  [ 0.0012,   0.0060]
  Pr(y=2|x):          0.0001    0.0000    0.0001  [-0.0000,   0.0002]
  Pr(y=3|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=4|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=5|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=6|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=7|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=8|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=9|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]

            postrally2  percent_ur~n  bermanest_~p   number_hate  violent_~10k  property~10k
Current=             1     18.503748      314.5418     24.595759     8.6605995     58.120767
  Saved=             0     18.503748      314.5418     24.595759     8.6605995     58.120767
   Diff=             1             0             0             0             0             0

          percent_r~12       college         south     northeast       midwest           jan
Current=     59.690604     20.783447     .45342935     .06919827     .33610587     .08333555
  Saved=     59.690604     20.783447     .45342935     .06919827     .33610587     .08333555
   Diff=             0             0             0             0             0             0

                   feb           mar           apr           may           jun           jul
Current=     .08333555     .08333555     .08333555     .08333555     .08333555     .08333555
  Saved=     .08333555     .08333555     .08333555     .08333555     .08333555     .08333555
   Diff=             0             0             0             0             0             0

                   aug           sep            oc           dec
Current=     .08333555     .08333555     .08333555     .08330897
  Saved=     .08333555     .08333555     .08333555     .08330897
   Diff=             0             0             0             0

. 
. nbreg n_hatecrim rallyt2      percent_urban bermanest_percap number_hate violent_crime_percap1
> 0k property_crime_percap10k percent_rep_pres12 college south northeast midwest jan feb mar apr
>  may jun jul aug sep oc dec, cluster(stcoufips)

Fitting Poisson model:

Iteration 0:   log pseudolikelihood = -103375.77  (not concave)
Iteration 1:   log pseudolikelihood = -95105.737  (not concave)
Iteration 2:   log pseudolikelihood = -87497.278  (not concave)
Iteration 3:   log pseudolikelihood = -84697.607  (not concave)
Iteration 4:   log pseudolikelihood = -81630.286  (not concave)
Iteration 5:   log pseudolikelihood = -80103.133  (not concave)
Iteration 6:   log pseudolikelihood = -78597.141  (not concave)
Iteration 7:   log pseudolikelihood =  -76944.93  (not concave)
Iteration 8:   log pseudolikelihood = -75404.283  
Iteration 9:   log pseudolikelihood = -71408.014  (backed up)
Iteration 10:  log pseudolikelihood = -31565.945  (backed up)
Iteration 11:  log pseudolikelihood =  -9709.891  (backed up)
Iteration 12:  log pseudolikelihood = -6931.3796  
Iteration 13:  log pseudolikelihood = -3680.2911  
Iteration 14:  log pseudolikelihood = -3553.2166  
Iteration 15:  log pseudolikelihood = -3504.4296  
Iteration 16:  log pseudolikelihood = -3503.4561  
Iteration 17:  log pseudolikelihood = -3503.4537  
Iteration 18:  log pseudolikelihood = -3503.4537  

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = -5748.2119  (not concave)
Iteration 1:   log pseudolikelihood = -4390.5166  
Iteration 2:   log pseudolikelihood = -4339.7709  
Iteration 3:   log pseudolikelihood = -4339.6961  
Iteration 4:   log pseudolikelihood = -4339.6961  

Fitting full model:

Iteration 0:   log pseudolikelihood = -3986.7129  (not concave)
Iteration 1:   log pseudolikelihood = -3485.3739  
Iteration 2:   log pseudolikelihood = -2900.4779  
Iteration 3:   log pseudolikelihood = -2875.3149  
Iteration 4:   log pseudolikelihood =  -2851.635  
Iteration 5:   log pseudolikelihood = -2851.1282  
Iteration 6:   log pseudolikelihood =  -2851.128  

Negative binomial regression                    Number of obs     =     37,631
                                                Wald chi2(22)     =     926.79
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood =  -2851.128               Pseudo R2         =     0.3430

                                      (Std. Err. adjusted for 3,137 clusters in stcoufips)
------------------------------------------------------------------------------------------
                         |               Robust
              n_hatecrim |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
                 rallyt2 |   .7758508   .1418956     5.47   0.000     .4977405    1.053961
           percent_urban |   .0692047   .0088609     7.81   0.000     .0518376    .0865717
        bermanest_percap |   .0002405   .0000497     4.83   0.000      .000143    .0003379
             number_hate |   .0185323   .0052316     3.54   0.000     .0082785     .028786
 violent_crime_percap10k |   .0087725   .0054086     1.62   0.105    -.0018281    .0193731
property_crime_percap10k |  -.0009049   .0021217    -0.43   0.670    -.0050634    .0032536
      percent_rep_pres12 |  -.0366479    .006407    -5.72   0.000    -.0492054   -.0240903
                 college |   .0341569   .0082791     4.13   0.000     .0179303    .0503836
                   south |  -.9174041   .3199471    -2.87   0.004    -1.544489   -.2903194
               northeast |   .5435587   .2947896     1.84   0.065    -.0342183    1.121336
                 midwest |  -.5341258    .277881    -1.92   0.055    -1.078762    .0105109
                     jan |  -1.179251   .1701771    -6.93   0.000    -1.512792   -.8457104
                     feb |  -1.205861   .1639231    -7.36   0.000    -1.527144   -.8845778
                     mar |  -.5840969   .1443305    -4.05   0.000    -.8669796   -.3012143
                     apr |  -.9056575   .1631101    -5.55   0.000    -1.225347   -.5859676
                     may |  -.9121138    .162441    -5.62   0.000    -1.230492   -.5937354
                     jun |  -.8349015   .1789661    -4.67   0.000    -1.185669   -.4841344
                     jul |  -1.281852    .191707    -6.69   0.000    -1.657591   -.9061128
                     aug |  -1.048403   .1566091    -6.69   0.000    -1.355351   -.7414547
                     sep |  -.9746501   .1564554    -6.23   0.000    -1.281297   -.6680032
                      oc |  -.6663172   .1366959    -4.87   0.000    -.9342363   -.3983981
                     dec |  -.3663063   .1648527    -2.22   0.026    -.6894117   -.0432008
                   _cons |  -4.793874   .5520443    -8.68   0.000    -5.875861   -3.711887
-------------------------+----------------------------------------------------------------
                /lnalpha |   1.290644   .1669811                      .9633665    1.617921
-------------------------+----------------------------------------------------------------
                   alpha |   3.635125   .6069973                      2.620504    5.042593
------------------------------------------------------------------------------------------

. prvalue, x(rallyt2=0) rest(mean) save

nbreg: Predictions for n_hatecrim

Confidence intervals by delta method

                                95% Conf. Interval
  Rate:               .00296   [ .00212,     .0038]
  Pr(y=0|x):          0.9971   [ 0.9962,    0.9979]
  Pr(y=1|x):          0.0029   [ 0.0021,    0.0037]
  Pr(y=2|x):          0.0000   [ 0.0000,    0.0000]
  Pr(y=3|x):          0.0000   [ 0.0000,    0.0000]
  Pr(y=4|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=5|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=6|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=7|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=8|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=9|x):          0.0000   [-0.0000,    0.0000]

         rallyt2  percent_ur~n  bermanest_~p   number_hate  violent_~10k  property~10k
x=             0     18.503748      314.5418     24.595759     8.6605995     58.120767

    percent_r~12       college         south     northeast       midwest           jan
x=     59.690604     20.783447     .45342935     .06919827     .33610587     .08333555

             feb           mar           apr           may           jun           jul
x=     .08333555     .08333555     .08333555     .08333555     .08333555     .08333555

             aug           sep            oc           dec
x=     .08333555     .08333555     .08333555     .08330897

. prvalue, x(rallyt2=1) rest(mean) diff

nbreg: Change in Predictions for n_hatecrim

Confidence intervals by delta method

                     Current     Saved    Change   95% CI for Change
  Rate:               .00642    .00296    .00347  [ 0.0013,   0.0056]
  Pr(y=0|x):          0.9937    0.9971   -0.0034  [-0.0055,  -0.0013]
  Pr(y=1|x):          0.0062    0.0029    0.0033  [ 0.0013,   0.0053]
  Pr(y=2|x):          0.0001    0.0000    0.0001  [ 0.0000,   0.0001]
  Pr(y=3|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=4|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=5|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=6|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=7|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=8|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=9|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]

               rallyt2  percent_ur~n  bermanest_~p   number_hate  violent_~10k  property~10k
Current=             1     18.503748      314.5418     24.595759     8.6605995     58.120767
  Saved=             0     18.503748      314.5418     24.595759     8.6605995     58.120767
   Diff=             1             0             0             0             0             0

          percent_r~12       college         south     northeast       midwest           jan
Current=     59.690604     20.783447     .45342935     .06919827     .33610587     .08333555
  Saved=     59.690604     20.783447     .45342935     .06919827     .33610587     .08333555
   Diff=             0             0             0             0             0             0

                   feb           mar           apr           may           jun           jul
Current=     .08333555     .08333555     .08333555     .08333555     .08333555     .08333555
  Saved=     .08333555     .08333555     .08333555     .08333555     .08333555     .08333555
   Diff=             0             0             0             0             0             0

                   aug           sep            oc           dec
Current=     .08333555     .08333555     .08333555     .08330897
  Saved=     .08333555     .08333555     .08333555     .08330897
   Diff=             0             0             0             0

. 
. nbreg n_hatecrim rallyt2      percent_urban bermanest_percap number_hate violent_crime_percap1
> 0k property_crime_percap10k percent_rep_pres12 college south northeast midwest jan feb mar apr
>  may jun jul aug sep oc dec, cluster(stcoufips) irr

Fitting Poisson model:

Iteration 0:   log pseudolikelihood = -103375.77  (not concave)
Iteration 1:   log pseudolikelihood = -95105.737  (not concave)
Iteration 2:   log pseudolikelihood = -87497.278  (not concave)
Iteration 3:   log pseudolikelihood = -84697.607  (not concave)
Iteration 4:   log pseudolikelihood = -81630.286  (not concave)
Iteration 5:   log pseudolikelihood = -80103.133  (not concave)
Iteration 6:   log pseudolikelihood = -78597.141  (not concave)
Iteration 7:   log pseudolikelihood =  -76944.93  (not concave)
Iteration 8:   log pseudolikelihood = -75404.283  
Iteration 9:   log pseudolikelihood = -71408.014  (backed up)
Iteration 10:  log pseudolikelihood = -31565.945  (backed up)
Iteration 11:  log pseudolikelihood =  -9709.891  (backed up)
Iteration 12:  log pseudolikelihood = -6931.3796  
Iteration 13:  log pseudolikelihood = -3680.2911  
Iteration 14:  log pseudolikelihood = -3553.2166  
Iteration 15:  log pseudolikelihood = -3504.4296  
Iteration 16:  log pseudolikelihood = -3503.4561  
Iteration 17:  log pseudolikelihood = -3503.4537  
Iteration 18:  log pseudolikelihood = -3503.4537  

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = -5748.2119  (not concave)
Iteration 1:   log pseudolikelihood = -4390.5166  
Iteration 2:   log pseudolikelihood = -4339.7709  
Iteration 3:   log pseudolikelihood = -4339.6961  
Iteration 4:   log pseudolikelihood = -4339.6961  

Fitting full model:

Iteration 0:   log pseudolikelihood = -3986.7129  (not concave)
Iteration 1:   log pseudolikelihood = -3485.3739  
Iteration 2:   log pseudolikelihood = -2900.4779  
Iteration 3:   log pseudolikelihood = -2875.3149  
Iteration 4:   log pseudolikelihood =  -2851.635  
Iteration 5:   log pseudolikelihood = -2851.1282  
Iteration 6:   log pseudolikelihood =  -2851.128  

Negative binomial regression                    Number of obs     =     37,631
                                                Wald chi2(22)     =     926.79
Dispersion           = mean                     Prob > chi2       =     0.0000
Log pseudolikelihood =  -2851.128               Pseudo R2         =     0.3430

                                      (Std. Err. adjusted for 3,137 clusters in stcoufips)
------------------------------------------------------------------------------------------
                         |               Robust
              n_hatecrim |        IRR   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
                 rallyt2 |    2.17244   .3082596     5.47   0.000        1.645    2.868993
           percent_urban |   1.071656   .0094959     7.81   0.000     1.053205     1.09043
        bermanest_percap |    1.00024   .0000497     4.83   0.000     1.000143    1.000338
             number_hate |   1.018705   .0053295     3.54   0.000     1.008313    1.029204
 violent_crime_percap10k |   1.008811   .0054562     1.62   0.105     .9981735    1.019562
property_crime_percap10k |   .9990955   .0021198    -0.43   0.670     .9949494    1.003259
      percent_rep_pres12 |   .9640155   .0061765    -5.72   0.000     .9519855    .9761976
                 college |   1.034747   .0085667     4.13   0.000     1.018092    1.051674
                   south |   .3995549   .1278364    -2.87   0.004     .2134209    .7480246
               northeast |   1.722125   .5076644     1.84   0.065     .9663605    3.068951
                 midwest |   .5861815   .1628887    -1.92   0.055     .3400161    1.010566
                     jan |   .3075089    .052331    -6.93   0.000      .220294    .4292523
                     feb |    .299434   .0490841    -7.36   0.000     .2171549    .4128885
                     mar |   .5576092     .08048    -4.05   0.000     .4202189    .7399192
                     apr |    .404276   .0659415    -5.55   0.000     .2936557    .5565671
                     may |   .4016743   .0652484    -5.62   0.000     .2921487    .5522605
                     jun |   .4339172   .0776565    -4.67   0.000     .3055418    .6162304
                     jul |   .2775229   .0532031    -6.69   0.000     .1905977     .404092
                     aug |    .350497    .054891    -6.69   0.000     .2578567    .4764203
                     sep |   .3773244   .0590344    -6.23   0.000     .2776769    .5127314
                      oc |   .5135966   .0702066    -4.87   0.000     .3928858    .6713947
                     dec |   .6932904   .1142908    -2.22   0.026     .5018712     .957719
                   _cons |   .0082803   .0045711    -8.68   0.000     .0028064    .0244314
-------------------------+----------------------------------------------------------------
                /lnalpha |   1.290644   .1669811                      .9633665    1.617921
-------------------------+----------------------------------------------------------------
                   alpha |   3.635125   .6069973                      2.620504    5.042593
------------------------------------------------------------------------------------------
Note: Estimates are transformed only in the first equation.
Note: _cons estimates baseline incidence rate.

. prvalue, x(rallyt2=0) rest(mean) save

nbreg: Predictions for n_hatecrim

Confidence intervals by delta method

                                95% Conf. Interval
  Rate:               .00296   [ .00212,     .0038]
  Pr(y=0|x):          0.9971   [ 0.9962,    0.9979]
  Pr(y=1|x):          0.0029   [ 0.0021,    0.0037]
  Pr(y=2|x):          0.0000   [ 0.0000,    0.0000]
  Pr(y=3|x):          0.0000   [ 0.0000,    0.0000]
  Pr(y=4|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=5|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=6|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=7|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=8|x):          0.0000   [-0.0000,    0.0000]
  Pr(y=9|x):          0.0000   [-0.0000,    0.0000]

         rallyt2  percent_ur~n  bermanest_~p   number_hate  violent_~10k  property~10k
x=             0     18.503748      314.5418     24.595759     8.6605995     58.120767

    percent_r~12       college         south     northeast       midwest           jan
x=     59.690604     20.783447     .45342935     .06919827     .33610587     .08333555

             feb           mar           apr           may           jun           jul
x=     .08333555     .08333555     .08333555     .08333555     .08333555     .08333555

             aug           sep            oc           dec
x=     .08333555     .08333555     .08333555     .08330897

. prvalue, x(rallyt2=1) rest(mean) diff

nbreg: Change in Predictions for n_hatecrim

Confidence intervals by delta method

                     Current     Saved    Change   95% CI for Change
  Rate:               .00642    .00296    .00347  [ 0.0013,   0.0056]
  Pr(y=0|x):          0.9937    0.9971   -0.0034  [-0.0055,  -0.0013]
  Pr(y=1|x):          0.0062    0.0029    0.0033  [ 0.0013,   0.0053]
  Pr(y=2|x):          0.0001    0.0000    0.0001  [ 0.0000,   0.0001]
  Pr(y=3|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=4|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=5|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=6|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=7|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=8|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]
  Pr(y=9|x):          0.0000    0.0000    0.0000  [-0.0000,   0.0000]

               rallyt2  percent_ur~n  bermanest_~p   number_hate  violent_~10k  property~10k
Current=             1     18.503748      314.5418     24.595759     8.6605995     58.120767
  Saved=             0     18.503748      314.5418     24.595759     8.6605995     58.120767
   Diff=             1             0             0             0             0             0

          percent_r~12       college         south     northeast       midwest           jan
Current=     59.690604     20.783447     .45342935     .06919827     .33610587     .08333555
  Saved=     59.690604     20.783447     .45342935     .06919827     .33610587     .08333555
   Diff=             0             0             0             0             0             0

                   feb           mar           apr           may           jun           jul
Current=     .08333555     .08333555     .08333555     .08333555     .08333555     .08333555
  Saved=     .08333555     .08333555     .08333555     .08333555     .08333555     .08333555
   Diff=             0             0             0             0             0             0

                   aug           sep            oc           dec
Current=     .08333555     .08333555     .08333555     .08330897
  Saved=     .08333555     .08333555     .08333555     .08330897
   Diff=             0             0             0             0

. 
. /* Table B */
. logit rally percent_urban n_hatecrim bermanest_percap number_hate violent_crime_percap10k prop
> erty_crime_percap10k percent_rep_pres12 college south northeast midwest jan feb mar apr may ju
> n jul aug sep oc, cluster(stcoufips) 

Iteration 0:   log pseudolikelihood = -1220.6978  
Iteration 1:   log pseudolikelihood = -1075.9077  
Iteration 2:   log pseudolikelihood =  -1027.887  
Iteration 3:   log pseudolikelihood = -1025.0663  
Iteration 4:   log pseudolikelihood = -1024.9977  
Iteration 5:   log pseudolikelihood = -1024.9975  
Iteration 6:   log pseudolikelihood = -1024.9975  

Logistic regression                             Number of obs     =     37,631
                                                Wald chi2(21)     =     419.46
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1024.9975               Pseudo R2         =     0.1603

                                      (Std. Err. adjusted for 3,137 clusters in stcoufips)
------------------------------------------------------------------------------------------
                         |               Robust
                   rally |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------------------+----------------------------------------------------------------
           percent_urban |   .0571683   .0068527     8.34   0.000     .0437372    .0705994
              n_hatecrim |   .0818234   .0765651     1.07   0.285    -.0682414    .2318882
        bermanest_percap |  -.0000967   .0000531    -1.82   0.069    -.0002009    7.44e-06
             number_hate |   .0054826   .0042716     1.28   0.199    -.0028896    .0138548
 violent_crime_percap10k |   .0074103   .0040344     1.84   0.066    -.0004971    .0153176
property_crime_percap10k |  -.0003473   .0012707    -0.27   0.785    -.0028377    .0021432
      percent_rep_pres12 |  -.0176378   .0047477    -3.71   0.000    -.0269431   -.0083324
                 college |   .0369608   .0070371     5.25   0.000     .0231684    .0507531
                   south |   .2771119   .2530324     1.10   0.273    -.2188224    .7730463
               northeast |   .8468852   .3160255     2.68   0.007     .2274866    1.466284
                 midwest |   .5460027   .2580381     2.12   0.034     .0402573    1.051748
                     jan |    2.54509   .4884563     5.21   0.000     1.587733    3.502447
                     feb |   2.271493   .4984268     4.56   0.000     1.294594    3.248392
                     mar |   2.304705   .4975617     4.63   0.000     1.329502    3.279908
                     apr |   2.511133   .4890824     5.13   0.000     1.552549    3.469717
                     may |    1.67722   .5318078     3.15   0.002     .6348956    2.719544
                     jun |   1.306992   .5616646     2.33   0.020     .2061494    2.407834
                     jul |   1.054494   .5901859     1.79   0.074    -.1022497    2.211237
                     aug |   1.957735     .51343     3.81   0.000      .951431    2.964039
                     sep |   1.682248   .5308571     3.17   0.002     .6417868    2.722708
                      oc |   2.352318   .4950171     4.75   0.000     1.382102    3.322534
                   _cons |  -9.186856   .6300731   -14.58   0.000    -10.42178   -7.951935
------------------------------------------------------------------------------------------

. 
. log close
      name:  <unnamed>
       log:  C:\Users\rpb0053\Dropbox\Ayal\Trump\data\PS_TrumpRallies.log
  log type:  text
 closed on:  22 Sep 2021, 10:59:52
------------------------------------------------------------------------------------------------
